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| The Measurement of Knowledge Flow Efficiency of Schools and Enterprises and Analysis of Influencing Factors |
| Wang Xiaohong1,Zhang Shaopeng1,Zhang Ben2 |
| (1.School of Economics and Management, Harbin Institute of Technology, Harbin 150001, China;2.School of Economics and Management,Harbin University of Science and Technology,Harbin 150080, China) |
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Abstract Based on dividing the university-industry knowledge flow process into the knowledge research phase and the knowledge conversion phase, this paper uses a two-stage DEA model to measure the efficiency of university-industry knowledge research and the efficiency of university-industry knowledge conversion. And based on the Tobit model of the spacial panel, this paper studies the two mechanisms of efficiency. The results show that: the overall efficiency of knowledge research in Chinese universities is higher than the efficiency of knowledge conversion, and there are regional imbalances in both types of efficiency, and the Moran Index verifies that both have positive space spillover effects; at the university level, the innovation ability of universities has a promoting effect on knowledge research efficiency but there is a suppressing effect on the efficiency of knowledge transformation, the structure of scientific research personnel only has a positive impact on the efficiency of knowledge conversion, government research support has a positive effect on the efficiency of knowledge research and the efficiency of knowledge conversion; at the provincial level, the level of provincial innovation concentration and the regional economic level have a significant positive impact on both the efficiency of knowledge research and the efficiency of knowledge conversion, the input of technological innovation only has a promoting effect on the efficiency of knowledge research; the influence mechanism of knowledge flow efficiency of university-industry is heterogeneous in different regions and different types of universities.
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Received: 02 June 2020
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| [1] 陈子韬,孟凡蓉,王焕.科技人力资源对科技创新绩效的影响:基于企业和高校机构的比较[J].科学学与科学技术管理,2017,38(9):173-180.[2] HUANG M H, CHEN D Z. How can academic innovation performance in university–industry collaboration be improved [J]. Technological Forecasting and Social Change, 2017,123(3): 210-215.[3] 王晓红,张奔.校企合作与高校科研绩效:高校类型的调节作用[J].科研管理,2018,39(2):135-142.[4] SOH P H, SUBRAMANIAN A M. When do firms benefit from university-industry R&D collaborations? the implications of firm R&D focus on scientific research and technological recombination [J]. Journal of Business Venturing, 2013, 29(6): 807-821.[5] 解学梅.中小企业协同创新网络与创新绩效的实证研究[J].管理科学学报,2010,13(8):51-64.[6] 张宝生,王晓红.虚拟科技创新团队知识流动意愿影响因素实证研究——基于知识网络分析框架[J].研究与发展管理,2012,24(2):1-9+57+126.[7] 王晰巍,赵树宽.知识供应链构建的绩效评价研究[J].图书情报工作,2009,53(16):108-111+132.[8] 宋曼祺,徐一旻,吕伟,等.政用产学研战略联盟知识价值链的研究[J].软科学,2018,32(2):96-100.[9] OJHA D, ACHARYA CH, COOPER D. Transformational leadership and supply chain ambidexterity: mediating role of supply chain organizational learning and moderating role of uncertainty[J]. International Journal of Production Economics, 2018,197(5): 215-231.[10] GUALANDRIS J, LEGENVRE H, KALCHSCHMIDT M. Exploration and exploitation within supply networks: examining purchasing ambidexterity and its multiple performance implications[J]. International Journal of Operations & Production Management, 2018(3): 667-689.[11] MOLLER K,RAJALA A.Rise of strategic nets-new modes of value creation[J]. Industrial Marketing Management, 2007,36(7): 895-908.[12] GUSTAVO DALMARCO, W HULSINK, PAULO ANTNIO ZAWISLAK. New perspectives on university-industry relations: an analysis of the knowledge flow within two sectors and two countries[J]. Technology Analysis & Strategic Management,2019,31(5): 1314-1326.[13] 王莉,游竹君.基于知识流动的创新生态系统价值演化仿真研究[J].中国科技论坛,2019(6):48-55.[14] 王文静,赵江坤.产学专利合作网络:结构演变与知识流动——合作形式视角[J].科技进步与对策,2019,36(7):1-9.[15] ZHANG B,WANG X.Empirical study on influence of university-industry collaboration on research performance and moderating effect of social capital: evidence from engineering academics in China[J]. Scientometrics, 2017, 113(4):1-21.[16] 张艺,孟飞荣,朱桂龙.海洋战略性新兴产业的产学研合作网络:特征、演化和影响[J].技术经济,2019,38(2):40-51.[17] 张艺,龙明莲,朱桂龙.科研团队视角下我国研究型大学参与产学研合作对学术绩效的影响[J]. 科技进步与对策,2019,36(1):132-141.[18] CHRISTIAN R，STERGAARD. Knowledge flows through social networks in a cluster: comparing university and industry links[J]. Structural Change and Economic Dynamics, 2008(10): 196-210.[19] 胡德鑫,王轶玮.基于DEA的“985”高校科研竞争力评价[J]. 北京理工大学学报(社会科学版),2017,19(4):163-168.[20] 张宝生,张庆普.虚拟科技创新团队知识流动效率影响因素的实证研究[J].情报科学,2016,34(2):70-76+132.[21] 赵晓萍,刘隽,古天龙,等.“双一流”建设背景下地方高校内部院系三阶段DEA效率评价[J].数学的实践与认识,2019,49(14):77-87.[22] 张卫国.我国高校科研生产率提升路径研究——基于31个省份的模糊集定性比较分析[J].中国高教研究,2019(7):78-84.[23] 沈能,彭慧,姚炯.多渠道国际研发溢出与创新效率空间收敛研究[J].科学学研究,2019,37(6):1091-1101+1141.[24] 赵增耀,章小波,沈能.区域协同创新效率的多维溢出效应[J].中国工业经济,2015(1):32-44.[25] 吴卫红,杨婷,张爱美.高校创新要素集聚对区域创新效率的溢出效应[J].科技进步与对策,2018,35(11):46-51.[26] 李彦华,张月婷,牛蕾.中国高校科研效率评价:以中国“双一流”高校为例[J].统计与决策,2019,35(17):108-111.[27] 郑丽霞,翟磊.产学研合作项目模式与高校科研绩效——R&D投入的中介作用[J].科技管理研究,2017,37(2):104-110.[28] 吴卫红,杨婷,张爱美,等.创新资源集聚对区域创新绩效的溢出效应——高校与高技术产业对比研究[J].科技进步与对策,2017,34(17):40-45.[29] 刘笑,陈强.产学合作数量与学术创新绩效关系[J].科技进步与对策,2017,34(20):51-56.[30] 王晓红,胡士磊.中国高校产学合作效率:测算及外部环境因素的影响[J].世界科技研究与发展,2017,39(12):503-510. |
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